code: def get_realigned_target_and_prediction_tensors_for_inference(
      targets: Dict[str, torch.Tensor],
      predictions: Dict[str, Dict[str, torch.Tensor]],
      of_name: str,
      tokenizer: PreTrainedTokenizer,
      pad_value: int = None,
  ) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
      """Realigns the target tensor with the predictions.
  
      This is necessary for text metrics that require the target and prediction to be of the same length.
  
      Args:
          targets: The target tensor.
          predictions: The prediction tensor.
          of_name: The output feature's name.
          tokenizer: The HF tokenizer.
          pad_direction: The direction to pad the tensors. Can be 'left' or 'right'.
              Defaults to 'right'.
  
      Returns:
          Tuple of realigned (targets, decoded_targets, predictions, decoded_predictions).
          - targets is a map of feature name -> tensor of token ids.
          - predictions is a map from output feature name -> map of tensors with the following items:
              - "predictions": tensor of token ids.
              - "probabilities": tensor of probabilities.
              - "logits": tensor of logits.
      """
      target_length = targets.get(of_name).size()[1]
      prediction_length = (
          predictions[of_name].get(PREDICTIONS).size()[1]
      )
  
      if target_length == prediction_length:
          return targets, predictions
  
      if not pad_value:
          pad_value = (
              tokenizer.pad_token_id
              if tokenizer.pad_token_id is not None
              else tokenizer.eos_token_id
          )
  
      zeros_to_add = (
          target_length - prediction_length
          if target_length > prediction_length
          else prediction_length - target_length
      )
  
      # We don't want to modify the original targets and predictions tensors, so we create a copy of them.
      _targets = copy.deepcopy(targets)
      _predictions = copy.deepcopy(predictions)
  
      # Align target and prediction tensors for text to text metric computation
      if target_length > prediction_length:
          # Pad the predictions.
          _predictions[of_name][PREDICTIONS] = F.pad(
              _predictions[of_name][PREDICTIONS],
              (0, zeros_to_add),
              value=pad_value,
          ).to(torch.int64)
  
          _predictions[of_name][PROBABILITIES] = F.pad(
              _predictions[of_name][PROBABILITIES],
              (0, 0, 0, zeros_to_add),
          ).to(torch.float32)
  
          _predictions[of_name][LOGITS] = F.pad(
              _predictions[of_name][LOGITS], (0, 0, 0, zeros_to_add)
          ).to(torch.float32)
      else:
          _targets[of_name] = F.pad(
              _targets[of_name], (0, zeros_to_add), value=pad_value
          ).to(torch.int64)
  
      return _targets, _predictions
  
imports: import copy
  import logging
  import tempfile
  from typing import Dict, Optional, Tuple, TYPE_CHECKING, Union
  import torch
  import torch.nn.functional
  import transformers
  from bitsandbytes.nn.modules import Embedding
  from packaging import version
  from transformers import AutoConfig, AutoModelForCausalLM, PreTrainedModel, PreTrainedTokenizer, TextStreamer
  from ludwig.constants import IGNORE_INDEX_TOKEN_ID, LOGITS, PREDICTIONS, PROBABILITIES
  from ludwig.schema.trainer import LLMTrainerConfig
  from ludwig.utils.error_handling_utils import default_retry
  from ludwig.utils.logging_utils import log_once
  from ludwig.utils.model_utils import find_embedding_layer_with_path
label_desc: Use image node_id 13 for calling a global function with example usage: get_realigned_target_and_prediction_tensors_for_inference(targets, predictions, of_name, tokenizer, pad_value) and returns: _targets,  _predictions, targets,  predictions